

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       unittest.TestCase				):





							"""simple docstring"""

							UpperCAmelCase_						:		List[str]								=	KandinskyVaaInpaintPipeline
							UpperCAmelCase_						:		Dict								=	['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
							UpperCAmelCase_						:		Optional[int]								=	[
							    '''image_embeds''',
							    '''negative_image_embeds''',
							    '''image''',
							    '''mask_image''',
							]
							UpperCAmelCase_						:		List[str]								=	[
							    '''generator''',
							    '''height''',
							    '''width''',
							    '''latents''',
							    '''guidance_scale''',
							    '''num_inference_steps''',
							    '''return_dict''',
							    '''guidance_scale''',
							    '''num_images_per_prompt''',
							    '''output_type''',
							    '''return_dict''',
							]
							UpperCAmelCase_						:		Dict								=	False




							@property
							def       snake_case			(					self						)		->       List[Any]:
														return 32




							@property
							def       snake_case			(					self						)		->       Tuple:
														return 32




							@property
							def       snake_case			(					self						)		->       Union[str, Any]:
														return self.time_input_dim




							@property
							def       snake_case			(					self						)		->       Tuple:
														return self.time_input_dim * 4




							@property
							def       snake_case			(					self						)		->       Optional[Any]:
														return 1_00




							@property
							def       snake_case			(					self						)		->       Dict:
														torch.manual_seed(0						)

														A      :					Optional[Any]      		=					{
														    '''in_channels''': 9,
														    # Out channels is double in channels because predicts mean and variance
														    '''out_channels''': 8,
														    '''addition_embed_type''': '''image''',
														    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
														    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
														    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
														    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
														    '''layers_per_block''': 1,
														    '''encoder_hid_dim''': self.text_embedder_hidden_size,
														    '''encoder_hid_dim_type''': '''image_proj''',
														    '''cross_attention_dim''': self.cross_attention_dim,
														    '''attention_head_dim''': 4,
														    '''resnet_time_scale_shift''': '''scale_shift''',
														    '''class_embed_type''': None,
														}

														A      :					List[Any]      		=					UNetaDConditionModel(**__UpperCAmelCase						)
														return model




							@property
							def       snake_case			(					self						)		->       List[str]:
														return {
														    "block_out_channels": [32, 64],
														    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
														    "in_channels": 3,
														    "latent_channels": 4,
														    "layers_per_block": 1,
														    "norm_num_groups": 8,
														    "norm_type": "spatial",
														    "num_vq_embeddings": 12,
														    "out_channels": 3,
														    "up_block_types": [
														        "AttnUpDecoderBlock2D",
														        "UpDecoderBlock2D",
														    ],
														    "vq_embed_dim": 4,
														}




							@property
							def       snake_case			(					self						)		->       Any:
														torch.manual_seed(0						)
														A      :					Dict      		=					VQModel(**self.dummy_movq_kwargs						)
														return model




							def       snake_case			(					self						)		->       List[str]:
														A      :					List[str]      		=					self.dummy_unet
														A      :					Optional[int]      		=					self.dummy_movq

														A      :					List[Any]      		=					DDIMScheduler(
														    num_train_timesteps=10_00    ,						beta_schedule='''linear'''    ,						beta_start=0.0_0_0_8_5    ,						beta_end=0.0_1_2    ,						clip_sample=__UpperCAmelCase    ,						set_alpha_to_one=__UpperCAmelCase    ,						steps_offset=1    ,						prediction_type='''epsilon'''    ,						thresholding=__UpperCAmelCase    ,						)

														A      :					Union[str, Any]      		=					{
														    '''unet''': unet,
														    '''scheduler''': scheduler,
														    '''movq''': movq,
														}

														return components




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=0						)		->       Tuple:
														A      :					int      		=					floats_tensor((1, self.text_embedder_hidden_size)    ,						rng=random.Random(__UpperCAmelCase						)						).to(__UpperCAmelCase						)
														A      :					Optional[Any]      		=					floats_tensor((1, self.text_embedder_hidden_size)    ,						rng=random.Random(seed + 1						)						).to(
														    __UpperCAmelCase						)
														# create init_image
														A      :					List[str]      		=					floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(__UpperCAmelCase						)						).to(__UpperCAmelCase						)
														A      :					Dict      		=					image.cpu().permute(0    ,						2    ,						3    ,						1						)[0]
														A      :					int      		=					Image.fromarray(np.uinta(__UpperCAmelCase						)						).convert('''RGB'''						).resize((2_56, 2_56)						)
														# create mask
														A      :					str      		=					np.ones((64, 64)    ,						dtype=np.floataa						)
														A      :					Dict      		=					0

														if str(__UpperCAmelCase						).startswith('''mps'''						):
																					A      :					Any      		=					torch.manual_seed(__UpperCAmelCase						)
														else:
																					A      :					Optional[Any]      		=					torch.Generator(device=__UpperCAmelCase						).manual_seed(__UpperCAmelCase						)
														A      :					Dict      		=					{
														    '''image''': init_image,
														    '''mask_image''': mask,
														    '''image_embeds''': image_embeds,
														    '''negative_image_embeds''': negative_image_embeds,
														    '''generator''': generator,
														    '''height''': 64,
														    '''width''': 64,
														    '''num_inference_steps''': 2,
														    '''guidance_scale''': 4.0,
														    '''output_type''': '''np''',
														}
														return inputs




							def       snake_case			(					self						)		->       Tuple:
														A      :					List[Any]      		=					'''cpu'''

														A      :					Optional[int]      		=					self.get_dummy_components()

														A      :					Dict      		=					self.pipeline_class(**__UpperCAmelCase						)
														A      :					Tuple      		=					pipe.to(__UpperCAmelCase						)

														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					Dict      		=					pipe(**self.get_dummy_inputs(__UpperCAmelCase						)						)
														A      :					Optional[int]      		=					output.images

														A      :					Dict      		=					pipe(
														    **self.get_dummy_inputs(__UpperCAmelCase						)    ,						return_dict=__UpperCAmelCase    ,						)[0]

														A      :					List[Any]      		=					image[0, -3:, -3:, -1]
														A      :					Tuple      		=					image_from_tuple[0, -3:, -3:, -1]

														print(f'image.shape {image.shape}'						)

														assert image.shape == (1, 64, 64, 3)

														A      :					Optional[int]      		=					np.array(
														    [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8]						)

														assert (
														    np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
														), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
														assert (
														    np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1E-2
														), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'






							def       snake_case			(					self						)		->       str:
														super().test_inference_batch_single_identical(expected_max_diff=3E-3						)







@slow
@require_torch_gpu
class 					__lowercase  (						unittest.TestCase				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       str:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()






							def       snake_case			(					self						)		->       Optional[int]:
														A      :					str      		=					load_numpy(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''						)

														A      :					List[str]      		=					load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''						)
														A      :					Union[str, Any]      		=					np.ones((7_68, 7_68)    ,						dtype=np.floataa						)
														A      :					int      		=					0

														A      :					Dict      		=					'''a hat'''

														A      :					int      		=					KandinskyVaaPriorPipeline.from_pretrained(
														    '''kandinsky-community/kandinsky-2-2-prior'''    ,						torch_dtype=torch.floataa						)
														pipe_prior.to(__UpperCAmelCase						)

														A      :					Optional[Any]      		=					KandinskyVaaInpaintPipeline.from_pretrained(
														    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''    ,						torch_dtype=torch.floataa						)
														A      :					Any      		=					pipeline.to(__UpperCAmelCase						)
														pipeline.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					List[Any]      		=					torch.Generator(device='''cpu'''						).manual_seed(0						)
														A		,		A      :					Dict      		=					pipe_prior(
														    __UpperCAmelCase    ,						generator=__UpperCAmelCase    ,						num_inference_steps=5    ,						negative_prompt=''''''    ,						).to_tuple()

														A      :					Union[str, Any]      		=					pipeline(
														    image=__UpperCAmelCase    ,						mask_image=__UpperCAmelCase    ,						image_embeds=__UpperCAmelCase    ,						negative_image_embeds=__UpperCAmelCase    ,						generator=__UpperCAmelCase    ,						num_inference_steps=1_00    ,						height=7_68    ,						width=7_68    ,						output_type='''np'''    ,						)

														A      :					str      		=					output.images[0]

														assert image.shape == (7_68, 7_68, 3)

														assert_mean_pixel_difference(__UpperCAmelCase    ,						__UpperCAmelCase						)









import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar







class 					__lowercase  (						unittest.TestCase				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       Union[str, Any]:
														A      :					Dict      		=					logging.get_logger()

														# the current default level is logging.WARNING
														A      :					List[Any]      		=					logging.get_verbosity()

														logging.set_verbosity_error()
														self.assertEqual(logger.getEffectiveLevel()    ,						logging.get_verbosity()						)

														logging.set_verbosity_warning()
														self.assertEqual(logger.getEffectiveLevel()    ,						logging.get_verbosity()						)

														logging.set_verbosity_info()
														self.assertEqual(logger.getEffectiveLevel()    ,						logging.get_verbosity()						)

														logging.set_verbosity_debug()
														self.assertEqual(logger.getEffectiveLevel()    ,						logging.get_verbosity()						)

														# restore to the original level
														logging.set_verbosity(__UpperCAmelCase						)




							def       snake_case			(					self						)		->       str:
														A      :					Any      		=					logging.get_verbosity()

														A      :					Optional[Any]      		=					logging.get_logger('''transformers.models.bart.tokenization_bart'''						)
														A      :					Tuple      		=					'''Testing 1, 2, 3'''

														# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
														if level_origin <= logging.WARNING:
																					with CaptureLogger(__UpperCAmelCase						) as cl:
																												logger.warning(__UpperCAmelCase						)
																					self.assertEqual(cl.out    ,						msg + '''\n'''						)

														# this is setting the level for all of `transformers.*` loggers
														logging.set_verbosity_error()

														# should not be able to log warnings
														with CaptureLogger(__UpperCAmelCase						) as cl:
																					logger.warning(__UpperCAmelCase						)
														self.assertEqual(cl.out    ,						''''''						)

														# should be able to log warnings again
														logging.set_verbosity_warning()
														with CaptureLogger(__UpperCAmelCase						) as cl:
																					logger.warning(__UpperCAmelCase						)
														self.assertEqual(cl.out    ,						msg + '''\n'''						)

														# restore to the original level
														logging.set_verbosity(__UpperCAmelCase						)




							@mockenv(TRANSFORMERS_VERBOSITY='''error'''						)
							def       snake_case			(					self						)		->       Optional[int]:
														# reset for the env var to take effect, next time some logger call is made
														transformers.utils.logging._reset_library_root_logger()
														# this action activates the env var
														A      :					int      		=					logging.get_logger('''transformers.models.bart.tokenization_bart'''						)

														A      :					Any      		=					os.getenv('''TRANSFORMERS_VERBOSITY'''    ,						__UpperCAmelCase						)
														A      :					List[str]      		=					logging.log_levels[env_level_str]

														A      :					Optional[int]      		=					logging.get_verbosity()
														self.assertEqual(
														    __UpperCAmelCase    ,						__UpperCAmelCase    ,						f'TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'    ,						)

														# restore to the original level
														A      :					str      		=					''''''
														transformers.utils.logging._reset_library_root_logger()




							@mockenv(TRANSFORMERS_VERBOSITY='''super-error'''						)
							def       snake_case			(					self						)		->       Optional[int]:
														# reset for the env var to take effect, next time some logger call is made
														transformers.utils.logging._reset_library_root_logger()
														A      :					str      		=					logging.logging.getLogger()
														with CaptureLogger(__UpperCAmelCase						) as cl:
																					# this action activates the env var
																					logging.get_logger('''transformers.models.bart.tokenization_bart'''						)
														self.assertIn('''Unknown option TRANSFORMERS_VERBOSITY=super-error'''    ,						cl.out						)

														# no need to restore as nothing was changed






							def       snake_case			(					self						)		->       Optional[int]:
														# testing `logger.warning_advice()`
														transformers.utils.logging._reset_library_root_logger()

														A      :					Union[str, Any]      		=					logging.get_logger('''transformers.models.bart.tokenization_bart'''						)
														A      :					Optional[int]      		=					'''Testing 1, 2, 3'''

														with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''1'''						):
																					# nothing should be logged as env var disables this method
																					with CaptureLogger(__UpperCAmelCase						) as cl:
																												logger.warning_advice(__UpperCAmelCase						)
																					self.assertEqual(cl.out    ,						''''''						)

														with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''''''						):
																					# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
																					with CaptureLogger(__UpperCAmelCase						) as cl:
																												logger.warning_advice(__UpperCAmelCase						)
																					self.assertEqual(cl.out    ,						msg + '''\n'''						)






def        snake_case__		(    ):
							disable_progress_bar()
							assert are_progress_bars_disabled()

							enable_progress_bar()
							assert not are_progress_bars_disabled()






